


import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


__UpperCamelCase     		=							{
    'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
    'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
    'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
    'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
    'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
    'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
    'mask_downscaling.0': 'mask_embed.conv1',
    'mask_downscaling.1': 'mask_embed.layer_norm1',
    'mask_downscaling.3': 'mask_embed.conv2',
    'mask_downscaling.4': 'mask_embed.layer_norm2',
    'mask_downscaling.6': 'mask_embed.conv3',
    'point_embeddings': 'point_embed',
    'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
    'image_encoder': 'vision_encoder',
    'neck.0': 'neck.conv1',
    'neck.1': 'neck.layer_norm1',
    'neck.2': 'neck.conv2',
    'neck.3': 'neck.layer_norm2',
    'patch_embed.proj': 'patch_embed.projection',
    '.norm': '.layer_norm',
    'blocks': 'layers',
}
def 							UpperCamelCase_(					_A :int	)->		Any:
			UpperCamelCase__       =							{}
			state_dict.pop("pixel_mean"					,  _A	)
			state_dict.pop("pixel_std"					,  _A	)

			UpperCamelCase__       =							r".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"

			for key, value in state_dict.items():
						for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
									if key_to_modify in key:
												UpperCamelCase__       =							key.replace(_A					,  _A	)

						if re.match(_A					,  _A	):
									UpperCamelCase__       =							int(re.match(_A					,  _A	).group(2	)	)
									if layer_nb == 0:
												UpperCamelCase__       =							key.replace("layers.0"					,  "proj_in"	)
									elif layer_nb == 1:
												UpperCamelCase__       =							key.replace("layers.1"					,  "layers.0"	)
									elif layer_nb == 2:
												UpperCamelCase__       =							key.replace("layers.2"					,  "proj_out"	)

						UpperCamelCase__       =							value

			UpperCamelCase__       =							model_state_dict[
			    "prompt_encoder.shared_embedding.positional_embedding"
			]

			return model_state_dict
def 							UpperCamelCase_(					_A :str					,  _A :Optional[int]					,  _A :List[str]					,  _A :str="ybelkada/segment-anything"	)->		Optional[int]:
			UpperCamelCase__       =							hf_hub_download(_A					,  F'''checkpoints/{model_name}.pth'''	)

			if "sam_vit_b" in model_name:
						UpperCamelCase__       =							SamConfig()
			elif "sam_vit_l" in model_name:
						UpperCamelCase__       =							SamVisionConfig(
						    hidden_size=10_24					,  num_hidden_layers=24					,  num_attention_heads=16					,  global_attn_indexes=[5, 11, 17, 23]					,  )

						UpperCamelCase__       =							SamConfig(
						    vision_config=_A					,  )
			elif "sam_vit_h" in model_name:
						UpperCamelCase__       =							SamVisionConfig(
						    hidden_size=12_80					,  num_hidden_layers=32					,  num_attention_heads=16					,  global_attn_indexes=[7, 15, 23, 31]					,  )

						UpperCamelCase__       =							SamConfig(
						    vision_config=_A					,  )

			UpperCamelCase__       =							torch.load(_A					,  map_location="cpu"	)
			UpperCamelCase__       =							replace_keys(_A	)

			UpperCamelCase__       =							SamImageProcessor()

			UpperCamelCase__       =							SamProcessor(image_processor=_A	)
			UpperCamelCase__       =							SamModel(_A	)

			hf_model.load_state_dict(_A	)
			UpperCamelCase__       =							hf_model.to("cuda"	)

			UpperCamelCase__       =							"https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
			UpperCamelCase__       =							Image.open(requests.get(_A					,  stream=_A	).raw	).convert("RGB"	)

			UpperCamelCase__       =							[[[4_00, 6_50]]]
			UpperCamelCase__       =							[[1]]

			UpperCamelCase__       =							processor(images=np.array(_A	)					,  return_tensors="pt"	).to("cuda"	)

			with torch.no_grad():
						UpperCamelCase__       =							hf_model(**_A	)
			UpperCamelCase__       =							output.iou_scores.squeeze()

			if model_name == "sam_vit_h_4b8939":
						assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

						UpperCamelCase__       =							processor(
						    images=np.array(_A	)					,  input_points=_A					,  input_labels=_A					,  return_tensors="pt"	).to("cuda"	)

						with torch.no_grad():
									UpperCamelCase__       =							hf_model(**_A	)
						UpperCamelCase__       =							output.iou_scores.squeeze()

						assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

						UpperCamelCase__       =							((75, 2_75, 17_25, 8_50),)

						UpperCamelCase__       =							processor(images=np.array(_A	)					,  input_boxes=_A					,  return_tensors="pt"	).to("cuda"	)

						with torch.no_grad():
									UpperCamelCase__       =							hf_model(**_A	)
						UpperCamelCase__       =							output.iou_scores.squeeze()

						assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

						# Test with 2 points and 1 image.
						UpperCamelCase__       =							[[[4_00, 6_50], [8_00, 6_50]]]
						UpperCamelCase__       =							[[1, 1]]

						UpperCamelCase__       =							processor(
						    images=np.array(_A	)					,  input_points=_A					,  input_labels=_A					,  return_tensors="pt"	).to("cuda"	)

						with torch.no_grad():
									UpperCamelCase__       =							hf_model(**_A	)
						UpperCamelCase__       =							output.iou_scores.squeeze()

						assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()
						__UpperCamelCase     		=							['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
						parser.add_argument(
						    '--model_name',
						    default='sam_vit_h_4b8939',
						    choices=choices,
						    type=str,
						    help='Path to hf config.json of model to convert',
						)
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model and processor to the hub after converting',
						)
						parser.add_argument(
						    '--model_hub_id',
						    default='ybelkada/segment-anything',
						    choices=choices,
						    type=str,
						    help='Path to hf config.json of model to convert',
						)

						__UpperCamelCase     		=							parser.parse_args()

						convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_big_bird import BigBirdTokenizer
else:
						__UpperCamelCase     		=							None

__UpperCamelCase     		=							logging.get_logger(__name__)
__UpperCamelCase     		=							{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase     		=							{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

__UpperCamelCase     		=							{
    'google/bigbird-roberta-base': 4_0_9_6,
    'google/bigbird-roberta-large': 4_0_9_6,
    'google/bigbird-base-trivia-itc': 4_0_9_6,
}


__UpperCamelCase     		=							'▁'







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		VOCAB_FILES_NAMES
			_UpperCamelCase		:       int 			=		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:       List[Any] 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase		:       Optional[Any] 			=		BigBirdTokenizer
			_UpperCamelCase		:       Dict 			=		['input_ids', 'attention_mask']
			_UpperCamelCase		:       List[int] 			=		[]




			def __init__(				self   ,    snake_case=None   ,    snake_case=None   ,    snake_case="<unk>"   ,    snake_case="<s>"   ,    snake_case="</s>"   ,    snake_case="<pad>"   ,    snake_case="[SEP]"   ,    snake_case="[MASK]"   ,    snake_case="[CLS]"   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else bos_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else eos_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else unk_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else pad_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else cls_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else mask_token

						super().__init__(
						    snake_case   ,    tokenizer_file=snake_case   ,    bos_token=snake_case   ,    eos_token=snake_case   ,    unk_token=snake_case   ,    sep_token=snake_case   ,    pad_token=snake_case   ,    cls_token=snake_case   ,    mask_token=snake_case   ,    **snake_case   ,    )

						UpperCamelCase__       =							vocab_file
						UpperCamelCase__       =							False if not self.vocab_file else True




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]
						if token_ids_a is None:
									return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = False  ):

						'''simple docstring'''


						if already_has_special_tokens:
									if token_ids_a is not None:
												raise ValueError(
												    "You should not supply a second sequence if the provided sequence of "
												    "ids is already formatted with special tokens for the model."  )
									return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is None:
									return [1] + ([0] * len(snake_case  )) + [1]
						return [1] + ([0] * len(snake_case  )) + [1] + ([0] * len(snake_case  )) + [1]




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]

						if token_ids_a is None:
									return len(cls + token_ids_a + sep  ) * [0]
						return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."  )

						if not os.path.isdir(snake_case  ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
									return
						UpperCamelCase__       =							os.path.join(
						    snake_case   ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

						if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case  ):
									copyfile(self.vocab_file   ,    snake_case  )

						return (out_vocab_file,)


